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Description: 图像处理源代码，包括分类程序和聚类程序。-image processing source code, including the classification procedure and clustering procedure.
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Author: 美髯客 |
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Description: 这是一个采用c++编写的用于机器学习文本分类的SVM算法的实现代码。-c prepared using machine learning for text classification of SVM codes.
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Author: pearllu |
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Description: 实现了从wave中读取数据，并通过mfcc提取特征系数。再通过分裂法聚类，最后使用EM算法建立GMM。-Realize the data read from the wave, and through MFCC feature extraction coefficient. Clustering through secession law, and finally the use of EM algorithm for the establishment of GMM.
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Size: 5120 |
Author: gujunjun |
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Description: 非线性分类，SVM理论为基础，调整参数改变分类（聚类的）边界的松紧程度-Nonlinear classification, SVM theory, adjust the parameters to change the classification (clustering) the degree of elastic boundary
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Size: 1024 |
Author: 高镇 |
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Description: 基于支持向量聚类的多聚焦图像融合算法∗
Exploiting SVC Algorithm for Multifocus Image Fusion-Based on support vector clustering algorithm for multi-focus image fusion
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Size: 711680 |
Author: 石磊 |
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Description: 数据挖掘，多种聚类算法.FCM, HCM, SVM, 等各种柔性聚类方法。-Data mining, a variety of clustering algorithms. FCM, HCM, SVM, such as flexible clustering method.
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Size: 8273920 |
Author: xuchen |
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Description: 统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含：
1，Analysis of linear discriminant function
2，Feature extraction: Linear Discriminant Analysis
3，Probability distribution estimation and clustering
4，Support Vector and other Kernel Machines-
This section should give the reader a quick overview of the methods implemented in
STPRtool.
• Analysis of linear discriminant function: Perceptron algorithm and multiclass
modification. Kozinec’s algorithm. Fisher Linear Discriminant. A collection
of known algorithms solving the Generalized Anderson’s Task.
• Feature extraction: Linear Discriminant Analysis. Principal Component Analysis
(PCA). Kernel PCA. Greedy Kernel PCA. Generalized Discriminant Analysis.
• Probability distribution estimation and clustering: Gaussian Mixture
Models. Expectation-Maximization algorithm. Minimax probability estimation.
K-means clustering.
• Support Vector and other Kernel Machines: Sequential Minimal Optimizer
(SMO). Matlab Optimization toolbox based algorithms. Interface to the
SVMlight software. Decomposition approaches to train the Multi-class SVM classifiers.
Multi-class BSVM formulation trained by Kozinec’s algorithm, Mitchell-
Demyanov-Molozenov algorithm
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Author: 查日东 |
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Description: 基于支持向量聚类的多聚焦图像融合算法.
从无监督机器学习角度提出了一种基于SVC(support vector clustering)的图像融合规则,解决了基于
SVM(support vector machine)的融合规则在处理多聚焦图像融合问题时所引起的区域混叠与非平滑过渡问题,进一步提高了融合图像的质量.-Based on support vector clustering algorithm for multi-focus image fusion. Never oversight perspective of Machine Learning Based SVC (support vector clustering) of the image fusion rules, to resolve based on SVM (support vector machine) the integration of the rules deal with multi-focus at image fusion problem arising from the regional-aliasing problem with non-smooth transition to further enhance the integration of image quality.
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Author: pzy |
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Description: 支持向量机一个很好的，较新的一个进行分析分析的软件-Recent advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely.Support Vector Machine is useful software
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Size: 27648 |
Author: chenyq |
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Description: 所上传文件包包括6种vc++源代码：使用opencv的实现可视图的静态路径规划；简单的svm算法；基于vc2008的图像匹配（多工程结构）；基于局部搜索的K-means聚类算法；三维匹配中的ICP算法；视觉tracking中的condensation算法-Upload file package, including the six kinds of vc++ source code: using opencv to view the realization of the static path planning svm simple algorithm vc2008-based image matching (multi-project structure) local search based on the K-means clustering algorithm three-dimensional matching of the ICP algorithm visual tracking of the condensation algorithm
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Size: 20701184 |
Author: zhezhe |
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Description: 用SVM算法实现聚类与分类的例程。内附实验数据，运行结果以及经典参考文献一篇《A New Fuzzy Cover Approach to Clustering》-SVM algorithm using clustering and classification routines. Included the experimental data, the results as well as the classic References 1 " A New Fuzzy Cover Approach to Clustering"
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Author: xinxin |
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Description: A distributed PSOSVM hybrid system with feature selection and parameter optimization
-Abstract
This study proposed a novel PSO–SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to
improve the classification accuracy with a small and appropriate feature subset. This optimization mechanism combined the discrete PSO with the
continuous-valued PSO to simultaneously optimize the input feature subset selection and the SVM kernel parameter setting. The hybrid PSO–SVM
data mining system was implemented via a distributed architecture using the web service technology to reduce the computational time. In a
heterogeneous computing environment, the PSO optimization was performed on the application server and the SVM model was trained on the
client (agent) computer. The experimental results showed the proposed approach can correctly select the discriminating input features and also
achieve high classification accuracy.
# 2007 Elsevier B.V. All rights reserved.
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Size: 565248 |
Author: alice |
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Description: svm 回归聚类函数包课用于支持向量机回归于分类-svm regression clustering function package
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Size: 22528 |
Author: 张超 |
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Description: 本程序包括：论文SVM 用于基于块划分特征提取的图像分类，和相应的matlab实现其中图像划分以及特征提取、聚类均利用matlab6.5完成。
-The procedures include: paper by SVM for feature extraction based on block classification, and the corresponding realization of one image into matlab, and feature extraction, clustering were done using matlab6.5.
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Author: darren |
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Description: 为了准确地对监控场景中的运动目标进行语义上的分类，提出了一种基于聚类的核主成分分析梯度方向直方图和二又决策树支持向量机的运动目标分类算法。-In order to accurately monitor the movement of scene targets semantic classification, the clustering based on kernel principal component analysis of gradient direction histograms, and two and a support vector machine decision tree classification algorithm of moving objects.
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Author: piano |
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Description: 基于神经网络的遥感图像分类取得了较好的效果,但存在固有的过学习、易陷入局部极小等缺点.支持向量机机器学习方法,根据结构风险最小化(SRM)原理,表现出很多优于其他传统方法的性能,本研究的基于多类支持向量机分类器的遥感图像分类取得了达95.4 的分类精度.但由于遥感图像分类类别多,所需训练样本较大,人工选择效率较低,为此提出以人工选择初始聚类质心、C均值模糊聚类算法自动标注训练样本的基于多类支持向量机的半监督式遥感图像分类方法,期望能在获得适用的分类精度的基础上有效提高分类效率-Neural net based remote sensing image classification has obtained good results. But neural net has inherent
flaws such as overfitting and local minimums. Support vector machine (SVM), which is based on Structural Risk Min-
imization(SRM), has shown much better performance than most other existing machine learning methods. Using mul-
ti-class SVM classifier high class rate of 95.4 is obtained. But for the class number of remote sensing image is much
great, manually obtaining of training samples is a much time-consuming work. So a multi-class SVM based semi-super-
vised approach is presented. It is choosed that the initial clustering centroids manually first, then label the samples as
the training ones automatically with fuzzy clustering algorithm. It is believed that this method will upgrade the classifi-
cation efficiency greatly with practicable class rate
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Size: 25600 |
Author: cissy |
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Description: 实现了 SVM 在医学图像分类中的应用，通过医学图像的实验结果验证了该方法的有效性。
对 SVM 算法进行了深入研究，结合量子行为的粒子群算法的优点，提出了一种量子行为的粒子群算法和 SVM 结合的混合分类模型，应用于医学图像的分类中。
深入研究了 K-means 算法，结合了无监督聚类和监督分类方法的优点，提出了一种 K-means 和 SVM 结合的混合分类模型，并应用于医学图像的分类中。
利用粒子群和量子行为的粒子群算法对 K-means 算法进行了改进，并将改进的K-means 算法与 SVM 算法结合形成全自动分类模型，并且把它应用于医学图像的分类中。
-SVM in the classification of medical image applications, medical images through the experimental results verify the effectiveness of the method. SVM algorithm conducted in-depth research, combined with the quantum behavior of the advantages of particle swarm optimization, PSO and SVM combined behavior of a quantum mixed classification model, applied to medical image classification. In-depth study of the K-means algorithm combines the advantages of unsupervised clustering and supervised classification method, propose a hybrid combination of a K-means and SVM classification model, and applied to the classification of medical images. Improved particle swarm algorithm using particle swarm and quantum behavior of the K-means algorithm, and improved K-means algorithm and SVM algorithm combined with the formation of a fully automatic classification model, and apply it to medical image classification.
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Size: 1626112 |
Author: 招伟杰 |
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Description: 使用MATLAB编程“支持向量机聚类”与 “支持向量机回归”-Support vector machine clustering and support vector machine regression
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Size: 1024 |
Author: 冯君淑 |
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Description: 基于MATLAB开发环境下的SVM机器学习聚类分析-SVM-based machine learning clustering analysis under MATLAB development environment
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Size: 5120 |
Author: 李佳楠 |
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Description: suport vector machin for clustering
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Size: 4904960 |
Author: majidjef
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